Abstract
This paper proposes a method for detecting errors concerning article usage and singular/plural usage based on the mass count distinction. Although the mass count distinction is particularly important in detecting these errors, it has been pointed out that it is hard to make heuristic rules for distinguishing mass and count nouns. To solve the problem, first, instances of mass and count nouns are automatically collected from a corpus exploiting surface information in the proposed method. Then, words surrounding the mass (count) instances are weighted based on their frequencies. Finally, the weighted words are used for distinguishing mass and count nouns. After distinguishing mass and count nouns, the above errors can be detected by some heuristic rules. Experiments show that the proposed method distinguishes mass and count nouns in the writing of Japanese learners of English with an accuracy of 93% and that 65% of article errors are detected with a precision of 70%.
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References
Kawai, A., Sugihara, K., Sugie, N.: ASPEC-I: An error detection system for English composition. IPSJ Journal 25, 1072–1079 (1984) (in Japanese)
McCoy, K., Pennington, C., Suri, L.: English error correction: A syntactic user model based on principled “mal-rule” scoring. In: Proc. 5th International Conference on User Modeling, pp. 69–66 (1996)
Schneider, D., McCoy, K.: Recognizing syntactic errors in the writing of second language learners. In: Proc. 17th International Conference on Computational Linguistics, pp. 1198–1204 (1998)
Pelletier, F., Schubert, L.: Two theories for computing the logical form of mass expressions. In: Proc. 10th International Conference on Computational Linguistics, pp. 108–111 (1984)
Izumi, E., Uchimoto, K., Saiga, T., Supnithi, T., Isahara, H.: Automatic error detection in the Japanese learners’ English spoken data. In: Proc. 41st Annual Meeting of the Association for Computational Linguistics, pp. 145–148 (2003)
Allan, K.: Nouns and countability. J. Linguistic Society of America 56, 541–567 (1980)
Baldwin, T., Bond, F.: A plethora of methods for learning English countability. In: Proc. 2003 Conference on Empirical Methods in Natural Language Processing, pp. 73–80 (2003)
Baldwin, T., Bond, F.: Learning the countability of English nouns from corpus data. In: Proc. 41st Annual Meeting of the Association for Computational Linguistics, pp. 463–470 (2003)
Bond, F., Vatikiotis-Bateson, C.: Using an ontology to determine English countability. In: Proc. 19th International Conference on Computational Linguistics, pp. 99–105 (2002)
O’Hara, T., Salay, N., Witbrock, M., Schneider, D., Aldag, B., Bertolo, S., Panton, K., Lehmann, F., Curtis, J., Smith, M., Baxter, D., Wagner, P.: Inducing criteria for mass noun lexical mappings using the Cyc KB, and its extension to WordNet. In: Proc. 5th International Workshop on Computational Semantics, pp. 425–441 (2003)
Lenat, D.B.: CYC: A large-scale investment in knowledge infrastructure. Communications of the ACM 38, 33–38 (1995)
Huddleston, R., Pullum, G.K.: The Cambridge Grammar of the English Language. Cambridge University Press, Cambridge (2002)
Rivest, R.L.: Learning decision lists. Machine Learning 2, 229–246 (1987)
Gillon, B.: The lexical semantics of English count and mass nouns. In: Proc. Special Interest Group on the Lexicon of the Association for Computational Linguistics, pp. 51–61 (1996)
Yarowsky, D.: Unsupervised word sense disambiguation rivaling supervised methods. In: Proc. 33rd Annual Meeting of the Association for Computational Linguistics, pp. 189–196 (1995)
Yarowsky, D.: Homograph Disambiguation in Speech Synthesis. Springer, Heidelberg (1996)
Burnard, L.: Users Reference Guide for the British National Corpus. version 1.0. Oxford University Computing Services, Oxford (1995)
Ostler, N., Atkins, B.: Predictable meaning shift: Some linguistic properties of lexical implication rules. In: Proc. of 1st SIGLEX Workshop on Lexical Semantics and Knowledge Representation, pp. 87–100 (1991)
Chodorow, M., Leacock, C.: An unsupervised method for detecting grammatical errors. In: Proc. 1st Meeting of the North America Chapter of the Association for Computational Linguistics, pp. 140–147 (2000)
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Nagata, R., Wakana, T., Masui, F., Kawai, A., Isu, N. (2005). Detecting Article Errors Based on the Mass Count Distinction. In: Dale, R., Wong, KF., Su, J., Kwong, O.Y. (eds) Natural Language Processing – IJCNLP 2005. IJCNLP 2005. Lecture Notes in Computer Science(), vol 3651. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11562214_71
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DOI: https://doi.org/10.1007/11562214_71
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